


'''simple docstring'''







from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef

import datasets


SCREAMING_SNAKE_CASE_   =      """\
@inproceedings{wang2019glue,
  title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},
  author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.},
  note={In the Proceedings of ICLR.},
  year={2019}
}
"""

SCREAMING_SNAKE_CASE_   =      """\
GLUE, the General Language Understanding Evaluation benchmark
(https://gluebenchmark.com/) is a collection of resources for training,
evaluating, and analyzing natural language understanding systems.
"""

SCREAMING_SNAKE_CASE_   =      """
Compute GLUE evaluation metric associated to each GLUE dataset.
Args:
    predictions: list of predictions to score.
        Each translation should be tokenized into a list of tokens.
    references: list of lists of references for each translation.
        Each reference should be tokenized into a list of tokens.
Returns: depending on the GLUE subset, one or several of:
    \"accuracy\": Accuracy
    \"f1\": F1 score
    \"pearson\": Pearson Correlation
    \"spearmanr\": Spearman Correlation
    \"matthews_correlation\": Matthew Correlation
Examples:

    >>> glue_metric = datasets.load_metric('glue', 'sst2')  # 'sst2' or any of [\"mnli\", \"mnli_mismatched\", \"mnli_matched\", \"qnli\", \"rte\", \"wnli\", \"hans\"]
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'accuracy': 1.0}

    >>> glue_metric = datasets.load_metric('glue', 'mrpc')  # 'mrpc' or 'qqp'
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'accuracy': 1.0, 'f1': 1.0}

    >>> glue_metric = datasets.load_metric('glue', 'stsb')
    >>> references = [0., 1., 2., 3., 4., 5.]
    >>> predictions = [0., 1., 2., 3., 4., 5.]
    >>> results = glue_metric.compute(predictions=predictions, references=references)
    >>> print({\"pearson\": round(results[\"pearson\"], 2), \"spearmanr\": round(results[\"spearmanr\"], 2)})
    {'pearson': 1.0, 'spearmanr': 1.0}

    >>> glue_metric = datasets.load_metric('glue', 'cola')
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'matthews_correlation': 1.0}
"""




def 							__lowercase    (   __SCREAMING_SNAKE_CASE					,	__SCREAMING_SNAKE_CASE		)					->       Tuple:
     """simple docstring"""




     return float((preds == labels).mean()		)




def 							__lowercase    (   __SCREAMING_SNAKE_CASE					,	__SCREAMING_SNAKE_CASE		)					->       int:
     """simple docstring"""




     __a   			= simple_accuracy(__SCREAMING_SNAKE_CASE					,	__SCREAMING_SNAKE_CASE		)
     __a   			= float(fa_score(y_true=__SCREAMING_SNAKE_CASE					,	y_pred=__SCREAMING_SNAKE_CASE		)		)
     return {
         "accuracy": acc,
         "f1": fa,
     }




def 							__lowercase    (   __SCREAMING_SNAKE_CASE					,	__SCREAMING_SNAKE_CASE		)					->       List[Any]:
     """simple docstring"""




     __a   			= float(pearsonr(__SCREAMING_SNAKE_CASE					,	__SCREAMING_SNAKE_CASE		)[0]		)
     __a   			= float(spearmanr(__SCREAMING_SNAKE_CASE					,	__SCREAMING_SNAKE_CASE		)[0]		)
     return {
         "pearson": pearson_corr,
         "spearmanr": spearman_corr,
     }



@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION		,     _KWARGS_DESCRIPTION   )
class 			lowerCAmelCase_    (							datasets.Metric   ):





  """simple docstring"""



  def __a    (       self  :				int    ):





       '''simple docstring'''

       if self.config_name not in [
           "sst2",
           "mnli",
           "mnli_mismatched",
           "mnli_matched",
           "cola",
           "stsb",
           "mrpc",
           "qqp",
           "qnli",
           "rte",
           "wnli",
           "hans",
       ]:
            raise KeyError(
                """You should supply a configuration name selected in """
                """[\"sst2\", \"mnli\", \"mnli_mismatched\", \"mnli_matched\", """
                """\"cola\", \"stsb\", \"mrpc\", \"qqp\", \"qnli\", \"rte\", \"wnli\", \"hans\"]"""    )
       return datasets.MetricInfo(
           description=_DESCRIPTION					,				citation=_CITATION					,				inputs_description=_KWARGS_DESCRIPTION					,				features=datasets.Features(
               {
                   """predictions""": datasets.Value("""int64""" if self.config_name != """stsb""" else """float32"""    ),
                   """references""": datasets.Value("""int64""" if self.config_name != """stsb""" else """float32"""    ),
               }    )					,				codebase_urls=[]					,				reference_urls=[]					,				format="""numpy"""					,				)


  def __a    (       self  :				Any					,				SCREAMING_SNAKE_CASE__  :				Optional[Any]					,				SCREAMING_SNAKE_CASE__  :				List[str]    ):





       '''simple docstring'''

       if self.config_name == "cola":
            return {"matthews_correlation": matthews_corrcoef(snake_case_					,				snake_case_    )}
       elif self.config_name == "stsb":
            return pearson_and_spearman(snake_case_					,				snake_case_    )
       elif self.config_name in ["mrpc", "qqp"]:
            return acc_and_fa(snake_case_					,				snake_case_    )
       elif self.config_name in ["sst2", "mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]:
            return {"accuracy": simple_accuracy(snake_case_					,				snake_case_    )}
       else:
            raise KeyError(
                """You should supply a configuration name selected in """
                """[\"sst2\", \"mnli\", \"mnli_mismatched\", \"mnli_matched\", """
                """\"cola\", \"stsb\", \"mrpc\", \"qqp\", \"qnli\", \"rte\", \"wnli\", \"hans\"]"""    )








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowerCamelCase_    :			Optional[int]    =	{
    """configuration_graphormer""": ["""GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GraphormerConfig"""],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCamelCase_    :			Union[str, Any]    =	[
							    """GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """GraphormerForGraphClassification""",
							    """GraphormerModel""",
							    """GraphormerPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_graphormer import GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, GraphormerConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_graphormer import (
														    GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    GraphormerForGraphClassification,
														    GraphormerModel,
														    GraphormerPreTrainedModel,
														)


else:
							import sys

							lowerCamelCase_    :			Optional[Any]    =	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






